{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)#10\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)#扁平化后矩阵中非零元素的位置,其实就是各类别位置\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)#各类选7个，7个绝对位置地址\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)#7*10\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))#[0,1,2...,4999]\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))#[0,1,2...,499]\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))#row:num,col:other\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 2**\n",
    "We can also other distance metrics such as L1 distance.\n",
    "The performance of a Nearest Neighbor classifier that uses L1 distance will not change if (Select all that apply.):\n",
    "1. The data is preprocessed by subtracting the mean.\n",
    "2. The data is preprocessed by subtracting the mean and dividing by the standard deviation.\n",
    "3. The coordinate axes for the data are rotated.\n",
    "4. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "[[3803.92350081 4210.59603857 5504.0544147  ... 4007.64756434\n",
      "  4203.28086142 4354.20256764]\n",
      " [6336.83367306 5270.28006846 4040.63608854 ... 4829.15334194\n",
      "  4694.09767687 7768.33347636]\n",
      " [5224.83913628 4250.64289255 3773.94581307 ... 3766.81549853\n",
      "  4464.99921613 6353.57190878]\n",
      " ...\n",
      " [5366.93534524 5062.8772452  6361.85774755 ... 5126.56824786\n",
      "  4537.30613911 5920.94156364]\n",
      " [3671.92919322 3858.60765044 4846.88157479 ... 3521.04515734\n",
      "  3182.3673578  4448.65305458]\n",
      " [6960.92443573 6083.71366848 6338.13442584 ... 6083.55504619\n",
      "  4128.24744898 8041.05223214]]\n",
      "[[3803.92350081 4210.59603857 5504.0544147  ... 4007.64756434\n",
      "  4203.28086142 4354.20256764]\n",
      " [6336.83367306 5270.28006846 4040.63608854 ... 4829.15334194\n",
      "  4694.09767687 7768.33347636]\n",
      " [5224.83913628 4250.64289255 3773.94581307 ... 3766.81549853\n",
      "  4464.99921613 6353.57190878]\n",
      " ...\n",
      " [5366.93534524 5062.8772452  6361.85774755 ... 5126.56824786\n",
      "  4537.30613911 5920.94156364]\n",
      " [3671.92919322 3858.60765044 4846.88157479 ... 3521.04515734\n",
      "  3182.3673578  4448.65305458]\n",
      " [6960.92443573 6083.71366848 6338.13442584 ... 6083.55504619\n",
      "  4128.24744898 8041.05223214]]\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' %(difference, ))\n",
    "print(dists_one)\n",
    "print(dists)\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "float64\n",
      "float64\n",
      "[[3803.92350081 4210.59603857 5504.0544147  ... 4007.64756434\n",
      "  4203.28086142 4354.20256764]\n",
      " [6336.83367306 5270.28006846 4040.63608854 ... 4829.15334194\n",
      "  4694.09767687 7768.33347636]\n",
      " [5224.83913628 4250.64289255 3773.94581307 ... 3766.81549853\n",
      "  4464.99921613 6353.57190878]\n",
      " ...\n",
      " [5366.93534524 5062.8772452  6361.85774755 ... 5126.56824786\n",
      "  4537.30613911 5920.94156364]\n",
      " [3671.92919322 3858.60765044 4846.88157479 ... 3521.04515734\n",
      "  3182.3673578  4448.65305458]\n",
      " [6960.92443573 6083.71366848 6338.13442584 ... 6083.55504619\n",
      "  4128.24744898 8041.05223214]]\n",
      "[[3803.92350081 4210.59603857 5504.0544147  ... 4007.64756434\n",
      "  4203.28086142 4354.20256764]]\n",
      "[[3803.92350081 4210.59603857 5504.0544147  ... 4007.64756434\n",
      "  4203.28086142 4354.20256764]\n",
      " [6336.83367306 5270.28006846 4040.63608854 ... 4829.15334194\n",
      "  4694.09767687 7768.33347636]\n",
      " [5224.83913628 4250.64289255 3773.94581307 ... 3766.81549853\n",
      "  4464.99921613 6353.57190878]\n",
      " ...\n",
      " [5366.93534524 5062.8772452  6361.85774755 ... 5126.56824786\n",
      "  4537.30613911 5920.94156364]\n",
      " [3671.92919322 3858.60765044 4846.88157479 ... 3521.04515734\n",
      "  3182.3673578  4448.65305458]\n",
      " [6960.92443573 6083.71366848 6338.13442584 ... 6083.55504619\n",
      "  4128.24744898 8041.05223214]]\n"
     ]
    }
   ],
   "source": [
    "M=np.tile(X_test[0,:],(5000,1))\n",
    "dif=X_train-M\n",
    "dists_twos=np.zeros([1,5000])\n",
    "dists_twos[0,:]=np.sqrt(np.sum(dif**2,axis=1)).T\n",
    "print(dists_one.dtype)\n",
    "print(dists_twos.dtype)\n",
    "print(dists.dtype)\n",
    "print(dists_one)\n",
    "print(dists_twos)\n",
    "print(dists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 725.404140 seconds\n",
      "One loop version took 128.679232 seconds\n",
      "No loop version took 1.090381 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):#当函数的参数不确定时，可以使用*args 和**kwargs，*args 没有key值，**kwargs有key值     \n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3]\n",
      "[[1 2 3 4]\n",
      " [5 6 7 8]\n",
      " [9 6 7 8]\n",
      " [7 6 7 8]]\n",
      "[[9 6 7 8]\n",
      " [7 6 7 8]]\n",
      "[]\n",
      "[1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 4, 4]\n"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2,3,4],[5,6,7,8],[9,6,7,8],[7,6,7,8]])\n",
    "S=np.array([[1,2,3,4],[5,6,7,8],[7,7,7,7],[7,7,7,7]])\n",
    "B=np.array_split(A, 2)\n",
    "all_idx=set(range(0,5))\n",
    "i=set([4])\n",
    "print(list(all_idx-i))\n",
    "print(A)\n",
    "print(B[1])\n",
    "C=np.array([1,2,3,4,5,6])\n",
    "print(C[7:])\n",
    "E=[1,2,3,4,5,6]\n",
    "F=[1,2,3,4,4,4]\n",
    "print(E+F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.239000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.240000\n",
      "k = 3, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.254000\n",
      "k = 5, accuracy = 0.248000\n",
      "k = 5, accuracy = 0.266000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.292000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 8, accuracy = 0.262000\n",
      "k = 8, accuracy = 0.282000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.276000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.295000\n",
      "k = 12, accuracy = 0.279000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.252000\n",
      "k = 15, accuracy = 0.289000\n",
      "k = 15, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.282000\n",
      "k = 15, accuracy = 0.274000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds=np.array_split(X_train,num_folds)#5-folds\n",
    "y_train_folds=np.array_split(y_train,num_folds)\n",
    "# for i in range(0,num_folds):\n",
    "#      X_train_folds.append(X_trFive[i])\n",
    "#      y_train_folds.append(y_trFive[i])\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "all_idx=set(range(0,num_folds))\n",
    "for i in k_choices:\n",
    "    k_to_accuracies[i]=[]\n",
    "    for j in range(0,num_folds):\n",
    "        X_tenew=X_train_folds[j]\n",
    "        y_tenew=y_train_folds[j]\n",
    "        X_trnew=np.reshape(X_train_folds[:j]+X_train_folds[j+1:],(int(X_train.shape[0] * (num_folds - 1) / num_folds), -1))\n",
    "        y_trnew=np.reshape(y_train_folds[:j]+y_train_folds[j+1:],int(y_train.shape[0]*(num_folds-1)/num_folds))\n",
    "        classifier.train(X_trnew, y_trnew)\n",
    "        dists_twonew = classifier.compute_distances_no_loops(X_tenew)\n",
    "        y_test_pred_new = classifier.predict_labels(dists_twonew, k=i)\n",
    "        num_correct = np.sum(y_test_pred_new == y_tenew)\n",
    "        accuracy = float(num_correct) / X_tenew.shape[0]\n",
    "        k_to_accuracies[i].append(accuracy)\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# Your code\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S1lkRuQQ4AYgXcX7Zqeq9YaxXn1WxeDElP70LLbgWAG9xMSU/dbYdz5szh2EfONNVv/7taQAkDU2n8sD+dvdJGpre6rh6fSnli7agTc4geVN5PeWLtgAE3RDJt4Lb6x3r1NFdwQ3YILcxJiShLMr7I3Al8ANAcNZdjA5zvfqs0gceROvqWpVpXR2lDzwY8Pzp864hOrb1eEZ0bBzT513Tquzw0h1oY+vZVNrYzOGlO4LWyVZwR0ZvWsVvzLEKpWVxpqrmi8gGVb1HRH4DLAp3xfoqb0ngfZY7KvfNenr+JWc2VFJ6RsDZUE3lgWdTdVTekq3gNsYcq1CChe/P5BoRyQEOAmPCV6W+LTo7G29x+/2Xo7M73n/5+Okzyf7U+Qv0+m9/K+A5npS4gIHBkxJ4llVLycnJAQODreA2xoQqlNlQi0UkBVgArAN2AE+Gs1J9WeZNNyLx8a3KJD6ezJtuPKb7Dpmdi8S0/rokJoohs3ODXmsruI0xx6rTloW76dEyVS0HnheRV4B4VbX+iw4kz5kDgLy2B21oIDonh8ybbvSXHy3fILY89wHa1IwnJY4hs3ODDm7DkUHsJc8W4vV6bT8LY0yXddqyUNVm4Dctjuu7EihE5EIRKRSRrSJye4DnbxaRT0Vkg4gsE5HRLZ67X0Q+EZGNIvI78U3DirD73r2PNfvWsGbfGk5+7GTue/e+duckz5lDwsknM+i005jwr2X+QNFRIkFnBXchRRs/ZuH3ru1wQd7gyZnEjkoibkwy2bdPDSlQ+OTn5zNixAhyc3O56aabLFAYY7oklG6o10XkS139ZS0iHuD3wEU4azSuEpFJbU5bDxSoaj7wHHC/e+2ZwFlAPnAicBowoyuvHw73vXsfTxc+7T9u1maeLnw6YMBoy5dIsMHbTK0oh8vqWP7EJv71txdtBbcxptcLJVjcjJM4sF5EDotIpYgcDuG6qcBWVd3u5pZ6Cpjb8gRVXe6mOgd4FxjhewqIB2KBOJx9NPaF8Jph9ezmwPkTOypvyZdIsDxKOeBR/m9IPe9IPe//8wVbwW2M6fWCzoZS1aPdPnU4sLvFcRFweifnXwe85r7mKhFZDpTgrO14ONDe3yJyPXA9wKhRo46ymqFr1uYulbdUVVZPpSg1AvHNkNwsrEjw8k7OZRxfuQmveIjWJv/5gVZwH4tXXnmFnTurUFXuued1pkyZwuc///lufQ1jTP8VNFiIyDmBylX1rWCXBrqsg9e4GijA7WoSkfHA8RxpabwhIue0fU1VXQgsBCgoKAh47+4UJVEBA0OUBG+gJabF8XZNFQCpzcK8qjj2eZp5X7by8ZATaCaKhKZaf9CIT0zstnq/8sorrFmzBtWJAKgqa9asAbCAYYwJSSjdULe2+PkpsBiYH8J1RcDIFscjgHYLEETkfOBO4FJV9fXHfAF4V1WrVLUKp8VxRgivGVaX513epfKWpnx+DB/GeYlXiHbj6HBPNBcffoev736cRG8VtdGDWJs8GQDtxtC3du1aABo1igb1UK+eVuXGGBNMKN1QreZ8ishI3IHoIFYDE0RkDLAHmAd8pc29JgOPABf6tmt17QK+JSK/wGmhzAAC58voQT854ycA/HWncxwlUVyed7m/vDMbY5uoERgmUdDstDSmzR3H4gdqSFQl2XuYJvGwJuVUJlZtIaU6lGGh0KgqG72ZlDEYEJ6sP5VBNJASVUvUK5+SNyyRCcOSmJCZSFJ8TND7GWMGnpASCbZRhDNDqVOq6hWR7wNLAQ/wF1X9RETuBdao6ss4C/0SgWfdyVa7VPVSnJlR5wEf4XRd/UNVFx9FXYN6cf0eFiwtpLi8lpyUBG6dPZHLJg/v8PyfnPETPvrQWW399DU/COk1VJW/vbODvGGJpCTEICIBEwkmN1ZQFpXGm+nTudr7Xrv7+LLOalMzJb98P6R1Fs3NyhrvSD72ZhFHIwk0Mi76IOXNCZRrAk+8t5O6FjmnhqckMGFYInnDktyfRMZnJjIo9mj+qQxcJXtf4vDhYpqb6/n3v3/E2HG3kJ01N/iFxvRSoYxZ/A9HxhqigFOAD0O5uaouAZa0KburxePzO7iuiR7YM+PF9Xu4Y9FH1DY6A8t7ymu5Y9FHAJ0GjK5aveMQnxQf5r+/cBIvfbCn1XPT513D6wsfBsBDM9PK3mNF+jk0nzKu1XlHk3W23tvELc9u4GNvFsd59lHWlIAInBTtrO8oKCjgoosvpOhQDZv3VbF5X6X7U8U7Ww/S4L6WCIxMHeRvgUwclsSEYYmMy0gkPsbTbZ9Tf1Gy9yU2bbqT5uZvAlBXX8ymTXcCWMAwfVYofy6uafHYCzypqv8OU3161IKlhf5A4VPb2MSCpYXdGiz+9s5nJCfE8IXJw9sFi7aJBKfFlVKSHMWftwpfqW0kOcHpFuos62ygYFFR28j1j63hvc/KuP2i4xh+WPn5Kmc2lIi0mg01euhgRg8dzOcmDfNf721qZmdZDVv2VVK4t4rNpZVs2VfJm4X78TY7fztECeQOHdymJZLEmPTBxEaHMhzWP23f9muam2tblTU317J9268tWJg+K5Rg8RxQ5/61j4h4RGRQi/URfVZxeW2Xyo/GnvJaln6yj29OH0NCbOC/wlsmEvzPb3+Ls/dUcOnDb7Ng6Sbuu+wkoGtZZ/eU13LtX9/nswPVPDTvFOaeMhwYx+PFzmvcHcIe3NGeKMZlOK2HC1t0OjZ4m9lxsNrfAtm8t5LNpZW88ek+3BhCdJSQmz7Y3wLxBZHcoYOI9vT/IFJXHzjDcEflxvQFoQSLZcD5QJV7nAC8DpwZrkr1lJyUBPYECAw5KQnd9hr/b5UzGn7NtNyQrzlxeDJfPzOXv72zgy+dOoLJo1JDzjr7afFhrv3b+9TUN/Hof0zlzHHp7a45FrHRUf5f/i3VNTaxfX81W0qdrqzCvVV8XFzBko9L/DO7Yj1RjM0Y7B8LmeDeZ1TaIDxRvSKbS7eIj8umrr595uH4uI4zDxvT24USLOLd6asAqGqViAwKY516zK2zJ7YaswBIiPFw6+yJ3XL/pmblyfd3MfuEYQzvYgC6+XN5LPmohDtf+JiXv38WQ2bnOmMULfYwapt19u0tB/jPx9eSFB/Ns9+ZxnFZQ7rlfYQiPsbDpJwhTMpp/Zq1DU1sLXXHQ0or2by3krU7D/Hyh0d+mcZFRzE+s/Wget6wJIanJBDVB4PI2HG3+McofKKiEhg77pYI1ciYYxdKsKgWkVNVdR2AiEwBuq+fJoJ84xJdmQ3VFQer6qmobeQbZ3Z9+4+k+BjmzzmB7zyxjkdX7eS6s517dJR1dtG6In703AbGZyby12tPIzu5+1pHxyIh1sNJI5I5aUTrvTOq6r1OENnrDqqXVrFq20FeWH9kTGdQrIcJma0H1fOGJZGdHE8vySsZkG9cImq1MxsqPi7HZkOZPi+UYHEjztRW35+C2TjbrPYLl00e3q2D2T6qyt7D9ZyQM4TTclOBI1lnm7zKoz/+N9PmjiPv9KwO73HhiVnMnJjBb18v5OKTssienEns+073T/a3p/pf53/f3MaCpYWcOW4of/zaFIb0gbUSiXHRnDIyhVNGprQqr6htZGupO6i+r5Itpc6g+nNri/znJMVFtxtUzxuWSEZSXK8JItlZcxkyxBkjOuusY9vLxJjeIJRFeatF5DhgIs4CuU2q2hjksgHvcJ2X2sYmvnFmLiLizzrbFOt04FeV1bP8iU0AHQYMEeHeuSdy/m9XcM/Ln/LHr01p9by3qZm7Xv6Ev7+3i8tOyeH+L5/c52chJSfEMGV0GlNGp7UqP1Td0Gpq7+Z9lSz9ZC9Prd7d6tq2g+p5wxIZmhh8N0FjTOdCWWfxPeAJVf3YPU4VkatU9X/DXrs+6lB1AzsP1hAdJcw5OQc4knWW2CPneRuaWfXStk5bFyPTBvHDWRNYsLSQZRuPJN6tafDyg7+vZ9mmUr577jhunT2x1/xVHQ6pg2M5fexQTh871F+mqhyoamCLG0QK91WxZV8liz8s5nCd13/e0MGxrQbVJ2YlkZeZRPKg3t8CM6a3CKUb6luq+nvfgaoeEpFvARYsAqioaeTqP79HnbeJicOS/IvWqsoCT33tqLylb00fy4vr93DXS5+QnRxPsypXLXyXj/ZU8LPLTuRrZ4wOeo/+SETISIojIymOM8cfmfWlquw7XN+iJeK0Rp5bW0R1w5HJDJlJcUzMSmJCZsvZWZbyxJhAQgkWUSIiqs4ESHdTo9gg1wxIlXWNXPPX99myr4q8zET/gjpwckEFCgyJacG7SGKjo7jvshO5cuG71HubqKzzIgKPfK2g1UI64xARspLjyUqO55y8DH+5qrKnvJYtbjdW4b5Ktuyr4u/vt055kpMc72+BTHBnaU0YZilPzMAWyr/+pcAzIvJHnLQf/wn8I6y16oOq671c+9fVfLKngj9cPYU/rdze6vlpc8f5xyh8omOjmDa3dVqPjpw+diiXTxnBs2uLiI4Snv3PaUweldpt9R8IRIQRqYMYkTqImccdWfXe3KzsbpHyZIvbpbVq+0EavEeCyMi0BPIyk8jLclsimUmMz7SUJ2ZgCCVY3IaTp+k7OAPcrwN/Cmel+prahiaue3Q163eX8z9XTeZzk4a1Cxa+cYknX/iAJq/6s852Nl7R1o0jM3hzXTHpzZD19y1Uh5BI0AQXFSUdpjzZVVbTalB9875KVmxunfJk9NDBTMhMdFoiblfW2PTu24/EmN4glNlQzcAf3B/TRl1jE9f/PycH04NXnsLFJ3W8Sjfv9CyGfeCsN/BlnQ1V9fpSePUzhqsziB1KIkFzbKI9UYzNSGRsm5QnjU3N7DhQTaEbRLa4XVrLNpXS5AYRT5QQ64kiKT6aZRv3MW3cUOvGMn1aKLOhJgC/ACbh7IsNgKqODWO9+oQGbzPffWIdK7cc4P4v57s5mMKjq4kETfjEeKKc/T/apDyp9zopT3wtkCfe3cWBqnque3QNsZ4oThuTyrl5mcyYmMGEzMR+PXvN9D+h/KnzV+Bu4AFgJnAtgbdMHVCaVfnBk+v416ZSfv6FE7miYGTwi45BVxIJduTpLrZmTNfERXs4PnsIx2c7KU/W7DhEsyo3np/His37WVG4n58v2cjPl2wkJzmeGRMzmJGXwZnj0/vEQkozsIUSLBJUdZk7I2onMF9EVuIEkAFJVdm+v5rVOw5x95xJfPX01lNXS+65h5rdzuDzxt99i5QrLif77mP7uEJNJGh6lygRzhqfzlnj0/nxxcdTXF7LW5v3s2Lzfl75sIQn39+NJ0qYMirVHzwmZQ/pkzmxTP8WSrCoE5EoYIu7890eYED3e+wqq+FgdQN3XHQc157VOu9TyT33UP7kU3D2d5yCpibnGI4pYISSSND0fjkpCcybOop5U0fR2NTM+l3lrNhcyorN+1mwtJAFSwtJT4xjRl4GMyZmMH18OqmDbaa6ibxQc0MNAn4I/AynK+rr4axUb3eoppGUQTF8e0b7aa/lzzwb8JryZ549pmDhG5foKJGg6XtiPFFMHZPG1DFp3Dr7OEor61i5+QArNu9n2aZ9PL+uiCiBk0emOMEjL4P8ESn9Kp276TtCyg3lPqzCGa8Y8LzNSlxHOZiamrpW3gWDAyQSNP1HZlI8X5oygi9NGUFTs7KhqJwVm/fzZuF+Hlq2hQf/uYXUQTFMn+AEjnPyMshIsm5I0zNsLh9w5SNOdtBQBoAbm5ppalaiozoIFh5P4MDgsYVbJnSeKGHyqFQmj0rlxvPzOFTdwMqtB3izsJS3Nh/w7wdy4vAhbqsjk1NHpQyInQhNZFiw6KJDNQ0AxHgCdwWkXHG5f4yibbkxRyt1cCyXnpzDpSfn0NysfFpy2D/D6o8rtvP75dtIio/m7PHp/vGO3rKniekfLFh0UXmNM8Ic3UG/sX9cwpc52+PpltlQpm8p2fsShw87mx/9+98/6tbNj6KihBOHJ3Pi8GS+N3M8h+saeWfrAd4sdGZZvfbxXgAmDkvyz7AqyE0lLtpat+bohbIoLwP4FpDb8nxV/Y/wVav3Kqt2WhadNfez776bQW7X1vF/+mGP1Mv0HiV7X2LTpjtpbv4mAHX1xf5tVsOxW96Q+BguPDHxBMevAAAgAElEQVSbC0/MRlXZUlrFm4XODKu//XsHC9/azqBYD2eOG8qMiZmcm5fByLR+sTOy6UGhtCxeAlYC/wSOfZS2jzvkCxY2I8V0YPu2X9Pc3Hrn4ebmWrZv+3XYt1YVEf/GT9efM47qei+rth10Bso3l/LPjaUAjE0fzDl5GZw7MYMzxg61ZIgmqFCCxSBVvS3sNekjDrndUDE2kGg6UFdf0qXycBocF835k4Zx/qRhqCo7Dtb4Wx1Pvr+Lv72zg7joKE4fO5Rz3bGOsemDLRWJaSeUYPGKiFysqku6enMRuRB4CPAAf1LVX7Z5/mbgm4AX2A/8h6ruFJGZOOlFfI4D5qnqi12tQ3fzDXD3tZbFhg0bWLZsGRUVFSQnJzNr1izy8/MjXa1+KT4um7r64oDlkSQijEkfzJj0MVx71hjqGpt477MyVhTuZ8XmUu595VN4BUakJjAjL4NzJ2YybdxQEuNsaLM3en5vGb/YXsKe+kaGx8Vwx9hsvpSVFvzCoxTKv4IbgB+LSANH1g+rqg7p7CJ3k6TfA58DioDVIvKyqn7a4rT1QIGq1ojId4D7gStVdTlwinufNGArTmr0iCurbiBK6FPpGDZs2MDixYtpbHS+voqKChYvXgxgASMMxo67xT9G4RMVlcDYcbdEqEaBxcd4/Iv9YBK7y2qcGVab9/Pi+j088d4uYjxCweg0zp3otDomDkuyVkcv8PzeMm4p3E2tm+W4qL6RWwqdWTXhChihLMpLCnZOB6YCW1V1O4CIPAXMBfzBwg0KPu8CVwe4z5eB11S15ijr0a0O1TR0vMail1q2bJk/UPg0NjaybNkyCxZh4BuXiFrtzIaKj8vp1tlQ4TIybRBXnzGaq88YTYO3mTU7y/zTc3/x2iZ+8domsobE+6fmnjU+vdVukKbn/GJ7iT9Q+NQ2K7/YXhK5YAEgIpcC57iHb6rqKyFcNpwjE0jBaV2c3sn51wGvBSifB/y2g3pdD1wPMGrUqBCqdOwOVTcQ3cEai1BEIvNrRUVFl8qNiY2O4sxx6Zw5Lp07LjqevRV1/gSISz4u4ek1TgLEU0el+BcFnpBjCRB7yp76xi6Vd4dQps7+EjgNeMItukFEzlbV24NdGqBMA5QhIlcDBcCMNuXZwEk4W7u2v5nqQmAhQEFBQcB7d7eymsYOF+T1VsnJyQEDQ3JycgRq0//19NTZnpCVHM8Vp43kitNG4m1q5oPdR1KR/Pr1zfz69c2kJ8ZyzgQ3AeKEDNIsAWLYDI+LoShAYBgeF76WXigti4uBU9wd8xCRR3HGGoIFiyKg5SYPI4B2o34icj5wJzBDVdvm4L4CeEFVwxcuu6i8D3ZDzZo1q9WYBUBMTAyzZs2KYK36r0hOne0J0Z4oCnLTKMhN478umMiBqnpWbnG6q97cvJ9F6/cgAvkjjiRAPGWkJUDsTneMzW41ZgGQECXcMTZ8kyhCneaQApS5j0P9c3Q1MEFExuCkNZ8HfKXlCSIyGXgEuFBVSwPc4yrgjhBfr0eUVTcwKLZvzUn3jUvYbKie0ZumzvaE9MQ4vjB5BF+Y7CRA/HhPhdvqKOXhf23hd8u2kJwQw/QJ6f7gkTkkPviNTYd84xK9bTbUL4D1IrIcp2vpHEL4Ba6qXnf/i6U4U2f/oqqfiMi9wBpVfRlYACQCz7ozLHap6qUAIpKL0zJZ0dU3FS6NTc1U1nn75K5m+fn5Fhx6SG+dOtsTPFHCySNTOHlkCj+cNYHymgbebpGK5JUNTsCclD3En4pkyuhUW7d0FL6UlRbW4NBWKLOhnhSRN3HGLQS4TVX3hnJzd23GkjZld7V4fH4n1+7AGSTvNfx5ofrYmIXpWX1l6mxPSBkUy+fzc/h8fg6qysaSSn+r4//e2s4f3txGYlw0Z40fygx3f/LhKZYAsTfqMFiIyHGquklETnWLitz/5ohIjqquC3/1ehd/xtkw9L3a/tj9R1+dOhtuIsKknCFMyhnCd84dR2VdI+9sO+i0OgpLWfrJPgAmZCb6FwWeNsYSIPYWnbUsbsaZlvqbAM8pcF5YatSLhZJE0BhwAsaQIU4yybPOujHCtemdkuJjmH1CFrNPyEJV2ba/yt9d9diqnfzp7c9IiPEwbdxQZ1FgXgajhw6OdLUHrA6Dhape7z68SFXrWj4nIgNydKq8j6b6MKa3ExHGZyYxPjOJb04fS02Dl3e3H3RTkeznX5uc+S+5Qwdx7sRMZuQ5CRAT+thkk74slAHud4BTQyjr98qqfWMW1rIwJpwGxUZz3nHDOO+4YQDsOFDtT0Xy1GonAWJsdBSnj0lzu6wyGJeRaKlIwqizMYssnAHmBHeKq+9bGAL0m2T4L67fw/pd5TQ0NXPWL//FrbMnctnk4bDhGVh2L1QUQfIImHUXh2qc+Lip7GMatYELnrubG069gUvGXhLhd2F6m3BufjQQ5aYPJjd9MF8/M5e6xiZW7yjztzrue3Uj9726keEpCf4ZVmeOG0pSH5y12FVd2RL6WHXWspgNfANnMV3LdBuVwI/DWKce8+L6Pdyx6CMampoB2FNeyx2LPmL47lc47aO7odFdWFWxGxb/kLVDfwaSTqM63VEl1SXMf2c+QLuAYQPWA5dvBfctU5x/P3X19PkV3L1JfIyH6ROcVeI/AYoO1fDW5gOs2FzKyx8U8/f3dhEdJRTkpjozrPIyOD7bEiAeq87GLB4FHhWRL6nq8z1Ypx6zYGkhtY2t93OqbWxi5LoFQOsVuDTWsqasBPG0ntZX11THQ+sestaF8evvK7h7mxGpg/jK6aP4yumjaPA2s27XIX8CxF/9YxO/+scmMpPi/AkQp4/PIHlQ/291dLdQ1lk8LyKXACcA8S3K7w1nxXpCcXltwPJM3R8ws1VN0yAkurpd+d7qkJadmAFioK3g7k1io6M4Y+xQzhg7lNsuPI7Sw3X+sY7XP93Hs2uLiBKYPCrVv5r8pOHJlgAxBKEkEvwjzhjFTOBPOCnD3w9zvXpETkoCewIEjFLJIIv97cqjmwbT6KkBbf2xZQ3OCun1Nr+3l1UvbaOqrJ7EtDimzR1H3umhXWv6joG8gru3yRwSz+UFI7m8wEmA+GFRhdvqKOWBf27mt29sJm1wLOdMSGfGxAzOmZDB0MS4SFe7VwplNtSZqpovIhtU9R4R+Q2wKNwV6wm3zp7I7c9voM7b7C9LiPGw+9RbyWo5ZgEQk0BiUyaV0VvwNh752OI98dxw6g1BX2vze3tZ/sQmvA3Oa1WV1bP8iU0AFjD6Gd8K7pZdUQN1BXdvEu2JYsroVKaMTuXmz+VxsKqet7ce8A+Uv/hBMSJw0vBk/wyrk0ek2OxHVyjBwvcvvkZEcoCDwJjwVannXDbZySZy49MfADA8JYFbZ0/ktMkXQm5qu9lQDYsSOWPESby/s4iG5gayB2eHPBtq1Uvb/IHCx9vQzKqXtlmw6Gd84xLbt/2auvoS4uOybTZULzQ0MY65pwxn7inDaW5WPik+zIrNpbxZuJ/fL9/K//xrK0Pio5k+IcM/3jFsACdADHUP7hScpH/rcFZv/ymstephQoCNNvKvcH5cjU3NHP77a0zOnkBdjZO86+kv/yDk16gqa5t9vfNy07dlZ8214NCHREUJJ41I5qQRyXz/vAlU1DTy720HeLOwlBWb9/PqR85403FZScyYmMG5eZlMGZ1KbPTAaXWEMsD9M/fh8yLyChCvqv1iizXf1FlfoPBNnYUjrQ4fXxLBo93QJTEtLmBgSEyz/lFjepvkQTFcfFI2F5+UjapSuK/S2a+jcD9/efszHlmxncGxHs4cn+5PRTIitd8sPwuos0V5X+zkOVS1z49bdDR1dsHSwgDBwllbkTro6ILFtLnjWo1ZAETHRjFt7rijup8xpmeICMdlDeG4rCF8e8Y4quq9vLP1gH+nwDc+dRIgjssYzIy8TM6dmMHUMWnEx/SvVCSdtSzmuP/NBM4E/uUezwTepB8Mcnc0dTZQuS+J4NEGC9+4hM2GMqZvS4yL5oITsrjATYC4/UC1PwHi4+/t5C///oz4GGcK77l5GcyYmMmY9O5PgHhb4S5WlVcBMHz5B1ydk8avJo7q9tfx6WxR3rUAbtfTJFUtcY+zgd+HrUY9KGVQDIdq2u/YmhJgwY4vPXnq4KNfzJN3epYFB2P6ERFhXEYi4zISue7sMdQ2NPHuZ04CxLc272f+4k9h8aeMHjrIv65j2rihDIoNdZPSwG4r3MWjxWX4/nRtAh4tdjYzDVfACKXGub5A4doH5IWlNj1M241qd1x+6BjHLIwx/V9CrIeZEzOZOTETgF0Ha1ix2Rkkf3ZNEY+t2kmsJ4qpbgLEGRMzmJDZ9QSIjxeXdVgeyWDxpogsBZ7EmTQ0D1geltr0sIra9q2KjsqPtRvKGDPwjBo6iK9Ny+Vr03Kp9zaxZseRVCQ/X7KRny/ZSE5yvD8B4lnj00NKgNjUxfLuEMpsqO+7g93T3aKFqvpCGOvUYzpawZ0TYFvHQ9UNJMR4Qhq0qli8mNIHHsRbUkJ0djaZN91I8pw5Qa8zxvRfcdEezhqfzlnj0/nxxcdTXF7LW24qklc+LOHJ93cTHSWcOvpIKpITcoYEbHV4CBwYwjmkHlLHmTvzqc8PaLd16+yJ3LHoo1YzohJiPNw6e2K7cw/VNIbUBVWxeDElP70LrXP2i/IWF1PyU2fb8e4IGJbN1pj+ISclgXlTRzFv6igam5pZv6vc32W1YGkhC5YWkpEUxzkTMtxUJOmkuD0bV+ek+ccoWro6Jy1s9e1s6uzbqnq2iFTSes2aAKqqQ8JWqx7imx77o+c20NDUTIxH+MUXT2o3bRacAe5AA99tlT7woD9Q+GhdHaUPPGitC2NMQDHuOMbUMWncOvs4SivrWLnZmZ67bNM+nl/nJEA8eWQKM/IyuGpiJs1ZytNuDjsPRHQ21Nnuf5PC9uq9wGWTh/Pk+7vYvr8apf1iPJ+y6oaQWhbeksCZRTsqN8aYtjKT4vnSlBF8acoImpqVDUXl/nUdDy3bwoP/3ELqoBgyRMhOjueVmaeEvU6dtSw6bc+oauDh+D4qNjqKPeW11HubiItu3/NXXtPAqLTgKzSjs7PxFrfPOBqdbRlHjTFd54kSJo9KZfKoVG48P49D1Q2s3OqkIln8YTHDhvRMFojOxizW4nQ/BZrTpcDYsNQoQuLcHC/7KuoZNbR9UCirbiA1hG6ozJtubDVmASDx8WTedGP3VdYYM2ClDo7l0pNzuPTkHIrKanrsdTvrhjrmzLIiciHwEE6X2p9U9Zdtnr8Z+CbgBfYD/6GqO93nRuEkLByJE5wuVtUdx1qnjvgSghVX1LYLFt6mZg7XeUkNoRvKNy5hs6GMMeHWk1vFhjQbSkRSgQm03invrSDXeHBWen8OKAJWi8jLqvppi9PWAwWqWiMi3wHuB650n3sM+LmqviEiiUDr/N7dLNbNWV9S0X4qbXlt1xbkJc+Z06XgsHHlclY+9RiVBw+QNDSd6fOu4fjpM0O+3hhjwi2UnfK+CdwAjAA+AM4AVgHnBbl0KrBVVbe793kKmAv4g4Wqtlzc9y5wtXvuJCBaVd9wz6sK8f0cNX/Loryu3XOH3AV5KWFYkLdx5XJeX/gw3gYnI23lgf28vvBhAAsYxpheI5Rk7DcApwE7VXUmMBkC7Dna3nBgd4vjIresI9cBr7mP84ByEVkkIutFZIHbUgkbT5SQMigmYMvCt3o7LQzBYuVTj/kDhY+3oZ6VTz3W7a9ljDFHK5RgUaeqdQAiEqeqm4D2q9ba62hgvP2JIlcDBTgbLIHT4pkO3IITqMYC3whw3fUiskZE1uzfH0r86lx2cgIlgVoWbl6oY0ki2JHKgwe6VG6MMZEQSrAocnfKexF4Q0ReAtrPDQ1wHc7gtM+IQNeJyPnAncClqlrf4tr1qrpdVb3ua5/a9lpVXaiqBapakJGREUKVOpeTHE9xxZFgceUjq7jykVVHMs6GoWWRNDS9S+XGGBMJQYOFqn5BVctVdT7wU+DPwGUh3Hs1MEFExohILE4CwpdbniAik4FHcAJFaZtrU0XEFwHOo8VYR7hkp8R32g0VjmAxfd41RMe2nicdHRvH9HnXdPtrGWPM0QplgPsh4GlVfUdVV4R6Y1X1isj3gaU4U2f/oqqfiMi9wBpVfRmn2ykReNadArZLVS9V1SYRuQVYJs4Ta4H/6/K766Ls5ATKaxqpbWgiIfbIEEl5jZNEsGVZd/ENYttsKGNMbxbK1Nl1wE9EJA94ASdwrAnl5qq6BFjSpuyuFo/P7+TaN4D8UF7nWPmS872wvghw1lqMy0j0P19W3RjSgryjdfz0mRYcjDG9WijdUI+q6sU4U2E3A78SkS1hr1kEZCc7qcnbDnIfqmkIaUGeMcb0V13Z2288cByQSw+MH0RCjhssituMWxyqaZ1E0NKEG2MGmlDGLH4FfBHYBjwD/ExVy8NdsZ7y4vo9LFhaSHF5LdnJzgL1di2L6gZGpAZPImiMMf1VKC2Lz4BpqtrvJv6/uH5Pq82PfNNmV20/wA1M8J93qKaRtDCOWRhjTG8XypjFH32BQkTmh71GPWjB0sJWu+T5rN91pOGkqlTUNtqYhTFmQAtlUV5Ll4alFhFSHGD/bYB675Gchd5mZ9F5ONZYGGPM0bqtcBeryqtYVV7F8OUfcFvhrrC+XleDRc/lw+0BOSkJActbvklvkxssrGVhjOklbivc1WoP7ibg0eKysAaMrgaLKWGpRYTcOnsiCTGtF9pFRwkKHK5z8kE1NjutjHAkETTGmKPxuBsoGqZm0DA1o115OAQNFiJyv4gMEZEYnNxQB9zEf33eZZOH84svnsTwlAQEGJ6SwFdOdzY8982I8rUsUmyA2xjTS7Qfae28vDuEMhvqAlX9kYh8ASfB3+XAcuDxMNarx1w2eTiXTT6SOX3tzjIeW7XTv9bC62tZWDeUMaaX8BA4MIRzH4dQuqF8f1JfDDypquFr5/QCbVdx+8csrBvKGNNLXJ2T1qXy7hBKy2KxiGwCaoHvuplg22/60E9kJsURJUe2V21sUuJjosKSRNAYY47GryY63eWPF5fRhNOiuDonzV8eDkGDhare7q7iPuxmg63G2R61X4r2RDFsSDzFn22Eol14G4eSFgVseAbyr4h09YwxBnACRjiDQ1uhDHBfDnjdQPETnLGKnLDXLIKyY6op2bUVvPV48ZCqh2DxD52AYYwxA1AoYxY/VdVKETkbmA08CvwhvNWKrOzKjylpTgFwgoVUQWMtLLs3wjUzxpjICCVY+AbdLwH+oKovAf16tDfHW0SxDkXVDRZUOk9UFEW2YsYYEyGhBIs9IvIIcAWwRETiQryuz8oe1EQ9sXjx0Eg0aeIGi+QRka2YMcZESCi/9K/A2Rr1Qjc1eRpwa1hrFWE5p1wAQD0xNOEhhSqISYBZdwW50hhj+qdQss7W4OxlMdvdUztTVV8Pe80iKDt/FgA1Hmdr1bRBHpjzO5sNZYwZsELZ/OgG4FvAIrfocRFZqKr/E9aaRVB2irMJUvWgkVBZT+rn74H8yE8Aq15fyuGlO2gqr8eTEseQ2bkMnpwZ6WoZYwaAUBblXQecrqrV4N85bxXQb4NF+uA4YjxCTYMztp/aC/JCVa8vpXzRFrTRST/SVF5P+SJnK3QLGMaYcAtlzEJonYakiX6WqrytqCghKzmemgYv0DtSfRxeusMfKHy0sZnDS3dEpkLGmAEllJbFX4H3ROQF9/gy4M/hq1LvkJ2cwO4yJ+VHb0gi2FRe36VyY4zpTqGk+/itiLwJnI3TorhWVdeHu2KRlpMc73/cG1oWnpS4gIHBkxIXgdoYYwaaToOFiEQBG1T1RGBdz1Spd8h2d9GLEnpFEsEhs3NbjVkASEwUQ2bnRq5SxpgBo9MxC1VtBj4UkaPKViUiF4pIoYhsFZHbAzx/s4h8KiIbRGSZiIxu8VyTiHzg/rx8NK9/LHwti+io3rH+cPDkTFK+OMHfkvCkxJHyxQk2uG2M6RGhjFlkA5+IyPtAta9QVS/t7CIR8QC/Bz6Hs2nSahF5WVU/bXHaeqBAVWtE5DvA/cCV7nO1qnpK6G+le/n2tYj29J6x/MGTMy04GGMiIpRgcc9R3nsqsFVVtwOIyFM4qc39wUJVl7c4/12g12zX6ltrER3Ve4KFMcZESofBQkTGA8NUdUWb8nOAPSHceziwu8VxEXB6J+dfB7zW4jheRNYAXuCXqvpigDpeD1wPMGpU9+Z1z3FbFjGe3tENZYwxkdTZb8IHwZdutZUa97lgAv1JrgFPFLkaKAAWtCgepaoFwFeAB0VkXLubqS5U1QJVLcjIyAihSqFL2foiHpqIqS6BB060vSyMMQNaZ8EiV1U3tC1U1TVAbgj3LgJGtjgeARS3PUlEzgfuBC5VVf/cUFUtdv+7HXgTmBzCa3aPDc8gr/yQ49lJDgegYrdtfmSMGdA6CxbxnTyXEMK9VwMTRGSMiMQC84BWs5pEZDLwCE6gKG1RnuqmQkdE0oGzaDHWEXbL7oXGWgZLHTHiLl63zY+MMQNYZ8FitYh8q22hiFwHrA12Y1X1At/HSW++EXhGVT8RkXtFxDeTagGQCDzbZors8cAaEfkQWI4zZtFzwaKjTY4qinh1+6tc8NwF5D+azwXPXcCr21/tsWoZY0ykdDYb6kbgBRH5KkeCQwHOLnlfCOXmqroEWNKm7K4Wj8/v4Lp3gJNCeY2wSB7hdD218WrGCOa/M5+6pjoASqpLmP/OfAAuGXtJT9bQGGN6VIctC1Xdp6pn4kyd3eH+3KOq01R1b89UL0Jm3eVsdtRSTAIPpab4A4VPXVMdD617qAcrZ4wxPS+U3FDLcbqCBg7fJkfP7gVvPSSPhFl3sXf9fQFP31vdv2OnMcbYIoKO5F8BI06D3LPhpo8h/wqyBmcFPLWjcmOM6S8sWHTBDafeQLyn9SSxeE88N5x6Q4RqZIwxPSOUdB/G5RvEfmjdQ+yt3kvW4CxuOPUGG9w2xvR7Fiy66JKxl1hwMMYMONYNZYwxJigLFsYYY4KyYGGMMSYoCxbGGGOCsmBhjDEmKAsWxhhjgrJgYYwxJigLFsYYY4KyYGGMMSYoCxbGGGOCsmBhjDEmKAsWxhhjgrJgYYwxJigLFsYYY4KyYGGMMSYoCxbGGGOCsmBhjDEmKAsWxhhjgrJgYYwxJqiwBgsRuVBECkVkq4jcHuD5m0XkUxHZICLLRGR0m+eHiMgeEXk4nPUMaMMzULQadrwND5zoHBtjzAAVtmAhIh7g98BFwCTgKhGZ1Oa09UCBquYDzwH3t3n+Z8CKcNWxQxuegcU/BG+9c1yx2zm2gGGMGaDC2bKYCmxV1e2q2gA8BcxteYKqLlfVGvfwXWCE7zkRmQIMA14PYx0DW3YvNNa2LmusdcqNMWYACmewGA7sbnFc5JZ15DrgNQARiQJ+A9za2QuIyPUiskZE1uzfv/8Yq9tCRVHXyo0xpp+LDuO9JUCZBjxR5GqgAJjhFn0XWKKqu0UC3ca9mepCYCFAQUFBwHsflYRUqC3j6bj72pcbY8wAFM5gUQSMbHE8Aihue5KInA/cCcxQVXeQgGnAdBH5LpAIxIpIlaq2GyQ3xhgTfuEMFquBCSIyBtgDzAO+0vIEEZkMPAJcqKqlvnJV/WqLc76BMwjec4Gi9lDXyo0xpp8L25iFqnqB7wNLgY3AM6r6iYjcKyKXuqctwGk5PCsiH4jIy+GqT5ckj+hauTHG9HOi2n1d/ZFUUFCga9as6Z6b+abOtpwRFZMAc34H+Vd0z2sYY0wvICJrVbUg2Hm2gjuQ/CucwJA8EhDnvxYojDEDWDjHLPq2/CssOBhjjMtaFsYYY4KyYGGMMSYoCxbGGGOCsmBhjDEmKAsWxhhjgrJgYYwxJigLFsYYY4KyYGGMMSaofpPuQ0T2Azu7eFk6cCAM1enN7D0PDPaeB45jfd+jVTUj2En9JlgcDRFZE0pOlP7E3vPAYO954Oip923dUMYYY4KyYGGMMSaogR4sFka6AhFg73lgsPc8cPTI+x7QYxbGGGNCM9BbFsYYY0JgwcIYY0xQAzJYiMiFIlIoIltF5PZI1yccRGSkiCwXkY0i8omI3OCWp4nIGyKyxf1vaqTr2t1ExCMi60XkFfd4jIi8577np0UkNtJ17G4ikiIiz4nIJvc7n9bfv2sRucn9t/2xiDwpIvH97bsWkb+ISKmIfNyiLOD3Ko7fub/XNojIqd1ZlwEXLETEA/weuAiYBFwlIpMiW6uw8AL/parHA2cA33Pf5+3AMlWdACxzj/ubG4CNLY5/BTzgvudDwHURqVV4PQT8Q1WPA07Gef/99rsWkeHAD4ECVT0R8ADz6H/f9d+AC9uUdfS9XgRMcH+uB/7QnRUZcMECmApsVdXtqtoAPAXMjXCdup2qlqjqOvdxJc4vj+E47/VR97RHgcsiU8PwEJERwCXAn9xjAc4DnnNP6Y/veQhwDvBnAFVtUNVy+vl3jbMtdIKIRAODgBL62Xetqm8BZW2KO/pe5wKPqeNdIEVEsrurLgMxWAwHdrc4LnLL+i0RyQUmA+8Bw1S1BJyAAmRGrmZh8SDwI6DZPR4KlKuq1z3uj9/3WGA/8Fe3++1PIjKYfvxdq+oe4NfALpwgUQGspf9/19Dx9xrW320DMVhIgLJ+O39YRBKB54EbVfVwpOsTTiLyeaBUVde2LA5wan/7vqOBU4E/qOpkoJp+1OUUiNtPPxcYA+QAg3G6Ydrqb991Z8L6b30gBosiYGSL4xFAcYTqElYiEoMTKJ5Q1UVu8T5f09T9b2mk6hcGZwGXisgOnO7F83BaGiluVwX0z++7CChS1ffc4+dwgkd//q7PBz5T1f2q2ggsAs6k/3/X0PH3GtbfbQMxWKwGJrizJmJxBsVejnCdup3bV/9nYKOq/rbFU74IlLgAAAHQSURBVC8DX3cffx14qafrFi6qeoeqjlDVXJzv9V+q+lVgOfBl97R+9Z4BVHUvsFtEJrpFs4BP6cffNU730xkiMsj9t+57z/36u3Z19L2+DFzjzoo6A6jwdVd1hwG5gltELsb5i9MD/EVVfx7hKnU7ETkbWAl8xJH++x/jjFs8A4zC+R/uclVtO4DW54nIucAtqvp5ERmL09JIA9YDV6tqfSTr191E5BScQf1YYDtwLc4fg/32uxaRe4ArcWb+rQe+idNH32++axF5EjgXJw35PuBu4EUCfK9u0HwYZ/ZUDXCtqq7ptroMxGBhjDGmawZiN5QxxpgusmBhjDEmKAsWxhhjgrJgYYwxJigLFsYYY4KyYGFMGIlIbsuMocb0VRYsjDHGBGXBwpgeIiJj3UR/p0W6LsZ0lQULY3qAm4rjeZxVtasjXR9juio6+CnGmGOUgZO/50uq+kmkK2PM0bCWhTHhV4Gzz8BZka6IMUfLWhbGhF8Dzm5mS0WkSlX/HukKGdNVFiyM6QGqWu1uzvSGiFSran9MnW36Mcs6a4wxJigbszDGGBOUBQtjjDFBWbAwxhgTlAULY4wxQVmwMMYYE5QFC2OMMUFZsDDGGBPU/weUNo/TK3oCqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 3**\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The training error of a 1-NN will always be better than that of 5-NN.\n",
    "2. The test error of a 1-NN will always be better than that of a 5-NN.\n",
    "3. The decision boundary of the k-NN classifier is linear.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "\n",
    "*Your explanation*:"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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